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Lecture
Understanding Statistics & Experimental Design
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Related lectures (30)
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Statistics for Data Science: Introduction to Statistical Methods
Covers the fundamental concepts of statistics and their application in data science.
Describing Data: Statistics and Hypothesis Testing
Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.
Hypothesis Testing: State of Nature
Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.
Understanding Statistics & Experimental Design
Covers basic probability theory, ANOVA, experimental design, and statistical reporting errors.
Linear Regression: Estimation and Testing
Explores linear regression estimation, hypothesis testing, and practical applications in statistics.
Hypothesis Testing in Statistics
Explores hypothesis testing, significance levels, errors, GWAS, optimal testing, and point estimation in statistics.
Two-Sample T-Test
Explains the two-sample t-test for comparing means of independent samples, including hypothesis testing steps and test statistic calculation.
Testing: t-tests
Covers t-tests, p-values calculation, and comparison of coefficients.
Describing Data: Statistics & Uncertainty
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
